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Paper WeB03.2

Herzog, Alexander (Max-Planck-Institute for Intelligent Systems, Tuebingen), Pastor, Peter (University of Southern California), Kalakrishnan, Mrinal (University of Southern California), Righetti, Ludovic (University of Southern California), Asfour, Tamim (Karlsruhe Institute of Technology (KIT)), Schaal, Stefan (University of Southern California)

Template-Based Learning of Grasp Selection

Scheduled for presentation during the Regular Session "Grasping: Learning and Estimation" (WeB03), Wednesday, May 16, 2012, 10:45−11:00, Meeting Room 3 (Mak'to)

2012 IEEE International Conference on Robotics and Automation, May 14-18, 2012, RiverCentre, Saint Paul, Minnesota, USA

This information is tentative and subject to change. Compiled on June 18, 2018

Keywords Grasping, Humanoid Robots, Learning and Adaptive Systems

Abstract

The ability to grasp unknown objects is an important skill for personal robots, which has been addressed by many present and past research projects. A crucial aspect of grasping is choosing an appropriate grasp configuration, i.e. the 6d pose of the hand relative to the object and its finger configuration. Finding feasible grasp configurations for novel objects, however, is challenging because of the huge variety in shape and size of these objects and the specific kinematics of the robotic hand in use. In this paper, we introduce a new grasp selection algorithm able to find object grasp poses based on previously demonstrated grasps. Assuming that objects with similar shapes can be grasped in a similar way, we associate to each demonstrated grasp a grasp template. The template is a local shape descriptor for a possible grasp pose and is constructed using 3d information from depth sensors. For each new object to grasp, the algorithm then finds the best grasp candidate in the library of templates. The grasp selection is also able to improve over time using the information of previous grasp attempts to adapt the ranking of the templates. We tested the algorithm on two different platforms, the Willow Garage PR2 and the Barrett WAM arm which have very different hands. Our results show that the algorithm is able to find good grasp configurations for a large set of objects from a relatively small set of demonstrations, and does indeed improve its performance over time.

 

 

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